WeightTransmitter: Weighted Association Rule Mining Using Landmark Weights

  • Yun Sing Koh
  • Russel Pears
  • Gillian Dobbie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)


Weighted Association Rule Mining (WARM) is a technique that is commonly used to overcome the well-known limitations of the classical Association Rule Mining approach. The assignment of high weights to important items enables rules that express relationships between high weight items to be ranked ahead of rules that only feature less important items. Most previous research to weight assignment has used subjective measures to assign weights and are reliant on domain specific information. Whilst there have been a few approaches that automatically deduce weights from patterns of interaction between items, none of them take advantage of the situation where weights of only a subset of items are known in advance. We propose a model, WeightTransmitter, that interpolates the unknown weights from a known subset of weights.


Weight Estimation Landmark Weights Association Rule Mining 


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  1. 1.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Using association rules for product assortment decisions: A case study. In: Knowledge Discovery and Data Mining, pp. 254–260 (1999)Google Scholar
  2. 2.
    Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: IDEAS 1998: Proceedings of the 1998 International Symposium on Database Engineering & Applications, pp. 68–77. IEEE Computer Society, Washington, DC (1998)Google Scholar
  3. 3.
    Koh, Y.S., Pears, R., Yeap, W.: Valency Based Weighted Association Rule Mining. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6118, pp. 274–285. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Pears, R., Sing Koh, Y., Dobbie, G.: EWGen: Automatic Generation of Item Weights for Weighted Association Rule Mining. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010, Part I. LNCS, vol. 6440, pp. 36–47. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Raileanu, L.E., Stoffel, K.: Theoretical comparison between the gini index and information gain criteria. Annals of Mathematics and Artificial Intelligence 41(1), 77–93 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Ramkumar, G.D., Sanjay, R., Tsur, S.: Weighted association rules: Model and algorithm. In: Proc. Fourth ACM Int’l Conf. Knowledge Discovery and Data Mining (1998)Google Scholar
  7. 7.
    Roiger, R.J., Geatz, M.W.: Data Mining: A Tutorial Based Primer. Addison Edu. Inc. (2003)Google Scholar
  8. 8.
    Sun, K., Bai, F.: Mining weighted association rules without preassigned weights. IEEE Trans. on Knowl. and Data Eng. 20(4), 489–495 (2008)CrossRefGoogle Scholar
  9. 9.
    Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: KDD 2003: Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining, pp. 661–666. ACM, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: KDD 2000: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 270–274. ACM, New York (2000)CrossRefGoogle Scholar
  11. 11.
    Yan, L., Li, C.: Incorporating Pageview Weight into an Association-Rule-Based Web Recommendation System. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 577–586. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yun Sing Koh
    • 1
  • Russel Pears
    • 2
  • Gillian Dobbie
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand
  2. 2.School of Computing and Mathematical SciencesAUT UniversityNew Zealand

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